Publication

SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

European Conference on Computer Vision (ECCV)


Abstract

LiDAR point-cloud segmentation is an important problem for many applications. For large-scale point cloud segmentation, the de facto method is to project a 3D point cloud to get a 2D LiDAR image and use convolutions to process it. Despite the similarity between regular RGB and LiDAR images, we are the first to discover that the feature distribution of LiDAR images changes drastically at different image locations. Using standard convolutions to process such LiDAR images is problematic, as convolution filters pick up local features that are only active in specific regions in the image. As a result, the capacity of the network is under-utilized and the segmentation performance decreases. To fix this, we propose Spatially-Adaptive Convolution (SAC) to adopt different filters for different locations according to the input image. SAC can be computed efficiently since it can be implemented as a series of element-wise multiplications, im2col, and standard convolution. It is a general framework such that several previous methods can be seen as special cases of SAC. Using SAC, we build SqueezeSegV3 for LiDAR point-cloud segmentation and outperform all previous published methods by at least 2.0% mIoU on the SemanticKITTI benchmark. Code and pretrained model are available at https://github.com/chenfengxu714/SqueezeSegV3.

Related Publications

All Publications

Acustico: Surface Tap Detection and Localization using Wrist-based Acoustic TDOA Sensing

Jun Gong, Aakar Gupta, Hrvoje Benko

ACM UIST - October 19, 2020

Learning Reasoning Strategies in End-to-End Differentiable Proving

Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel

ICML - August 13, 2020

Voice Separation with an Unknown Number of Multiple Speakers

Eliya Nachmani, Yossi Adi, Lior Wolf

ICML - October 1, 2020

Constraining Dense Hand Surface Tracking with Elasticity

Breannan Smith, Chenglei Wu, He Wen, Patrick Peluse, Yaser Sheikh, Jessica Hodgins, Takaaki Shiratori

SIGGRAPH Asia - December 1, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy